8 results about "Graduate students" patented technology

The invention relates to the technical field of archive management, in particular to an electronic archive data management method and system. According to the method, first-level identity verification is carried out on login information input when a previous graduate logs in an electronic archive data management system, and after the first-level identity verification is passed, a first permission is opened to the previous graduate. Secondly, when the graduation thesis file management platform needs to modify the graduation thesis electronic file, the graduation thesis file management platform receives a modification application submitted by the previous graduate; and thirdly, the graduation thesis archive management platform identifies areas needing to be modified in the modification application, performs identity verification of different levels on the previous graduates according to different modification areas, and opens corresponding permissions after the identity verification succeeds. And finally, the graduation thesis archive management platform audits the modified electronic archive. The invention provides an electronic archive data management method and system. The information security of graduation thesis electronic archives can be improved.

The invention provides a university graduate employment direction prediction method and device, and relates to the technical field of employment direction prediction, and the method comprises the steps: collecting the professional class weighted average score, the public class weighted average score, the occupational character type and the library access frequency of the previous graduates; calculating an employment direction success coefficient, an employment direction satisfaction coefficient and an employment direction prediction coefficient of the previous graduates; and comparing and judging the employment direction prediction coefficient with an employment direction evaluation threshold so as to determine a recommended employment direction and recommend the recommended employment direction to graduates. According to the invention, comprehensive three-dimensional analysis is carried out on the graduates from four dimensions of the weighted average score of the professional class, the weighted average score of the public class, the occupational character type and the number of times of entry and exit of the library; and an important reference factor of employment direction satisfaction of previous students is introduced, so that the precision and practicability of graduate employment direction prediction are improved.

1. Name of the product in this design: Admission Notice (Multi-faceted Three-dimensional Interactive Display). 2. Purpose of this design: To create a multi-faceted, three-dimensional graduate admission notice that commemorates the entire process from admission to graduation. 3. The key design features of this product are the combination of shape and pattern. 4. The image or photograph that best illustrates the design's key points: a 3D model. 5. The bottom surface of this product is a part that is not easily seen or cannot be seen during use, so the bottom view is omitted.

A computer-based system for determining the degree of conformity with a graduate profile of an academic program based on academic performance data, wherein the system comprises the following: a communication interface unit configured to receive academic data from remote computer terminals connected to an academic unit via a communication network; a storage unit that is operationally connected to the communication interface unit and is configured to store graduate profile data, training area identifiers, subject identifiers, semester identifiers and subject grade values; a profile processing unit connected to the storage unit and configured to generate an education area data matrix by storing a variety of education areas for graduate profiles in the storage unit, each education area corresponding to a competency area associated with the completion of the academic program; a curriculum mapping processing unit coupled to the storage unit, configured to generate a mapping table between subjects and training areas by mapping each subject of a curriculum to at least one training area for graduate profiles stored in the storage unit; a grade processing unit connected to the communication interface unit and the storage unit, configured to receive semester grade values ​​corresponding to subjects in the curriculum and to generate a semester grade record by storing the received grade values ​​in the storage unit along with subject and semester identifiers; a contribution calculation unit connected to and configured with the storage unit, which generates a student contribution matrix representing the proportional contribution values ​​of the students in relation to the respective training areas of the university profile; a semester assessment unit configured to determine a semester performance value for each area of ​​study within the graduate profile, based on the subject contribution matrix and the semester grade data set; and A control unit connected to the semester assessment unit and configured to determine a cumulative conformance score representing the overall degree of attainment of the graduate profile over several semesters, wherein the contribution calculation unit is configured to generate the subject contribution matrix using a matrix generation circuit that stores subject identifiers along a first dimension and identifiers of the graduate profile's areas of study along a second dimension of the matrix, each matrix element representing a proportional contribution value corresponding to a subject's participation in a respective area of ​​study, and wherein the control unit is configured to classify the cumulative conformance score into a variety of predefined conformance levels based on stored threshold ranges representing the graduate profile's performance categories.

The invention discloses a big language model feedback and expert review fusion-based academic paper innovativeness automatic determination method. The method comprises the steps of obtaining an academic paper and an expert review result and performing preprocessing; an academic paper innovativeness automatic judgment model is established, and a sparse attention module is used for obtaining a sparse attention weight to perform feature fusion; the feedforward neural network module is used for performing nonlinear feature enhancement and dimension transformation processing on input data; the attention feature dimension reduction module is used for extracting global semantic information in the enhanced features; the classifier module is used for outputting a prediction result of the paper innovativeness category; and realizing automatic judgment of the innovation level of the academic papers based on the trained academic paper innovation automatic judgment model. According to the invention, systematic modeling and semantic analysis are carried out on academic papers and expert comment data over the years, so that the scientific and intelligent level of innovative evaluation of papers can be improved, and data support and decision reference can be provided for postgraduate cultivation quality monitoring, academic direction optimization and scientific research and innovation ability improvement.

The invention discloses a postgraduate student voluntary reporting decision-making method and system based on score prediction, and belongs to the technical field of education data mining and decision-making support. According to the method, historical academic features and campus behavior time series data of students are fused and processed, and a conditional flow model is used to predict complete probability distribution and confidence interval of examination results; further, sampling the prediction distribution, and carrying out cross-year standardized comparison and weighted quantile ranking calculation on the sampling score and the score enrollment of the target college over the years to obtain comprehensive ranking estimation and interval reflecting uncertainty; and finally, according to the ranking interval, generating a personalized ''impulsive, stable and guarantee'' volunteer filling strategy suggestion. According to the method, a decision closed loop from probability prediction to risk quantitative ranking is realized, and the scientificity and individuation level of volunteer filling are remarkably improved.

The invention discloses a graduate student full-cycle cultivation management method and system, and relates to the field of educational informatization and artificial intelligence, and the method comprises the steps: obtaining the multi-source data and tutor academic labels of students; extracting key features in the obtained multi-source data by adopting a natural language processing technology NLP, determining a student research direction, and performing time sequence analysis on the obtained time sequence data by adopting an LSTM model to determine a student growth curve; according to the student growth curve, in combination with the student research direction and the tutor academic label, determining the adaptation degree of the student and the target tutor group, and according to the adaptation degree of the student and the target tutor group, generating a tutor-student matching weight matrix; and with maximization of the overall scientific research output of the research group as a target function, optimizing the tutor-student matching weight matrix by adopting a game theory algorithm based on Nash equilibrium to obtain an optimal allocation result.

This invention discloses a big data management system and method for the entire process of postgraduate training, relating to the field of data management. It includes a multi-source assessment data collection module, an assessment weakness identification and monitoring module, a related literature matching and recommendation module, a multi-weak-area assessment trajectory monitoring module, a periodic tutor comprehensive evaluation collection module, a learning process feedback and push module, and an information management platform. The system pre-collects multi-source assessment scores for each postgraduate student, divides each assessment node into blocks according to different assessment content, performs refined identification and quantitative evaluation of weakness areas in each block, and simultaneously implements personalized literature recommendations based on the assessment content of the weak areas and dynamic monitoring of weak areas under continuous assessment nodes, quantifying the degree of improvement of weak areas under continuous assessment nodes. Combined with periodic tutor evaluation data for postgraduate students, it achieves intelligent monitoring of the postgraduate training process.